Xiaohua Dong

Dynamic Application and Risk Analysis of Flood Control Water Level to the Three Gorges Reservoir by Utilizing Mid-Term Inflow Forecasts

The objective of this study is to carry out reliability and risk analyses of a methodology of dynamically applying Flood Control Level (FCL) within a constraint boundary for the Three Gorges Reservoir (TGR) in middle China. The dynamic application model is based on the mid-term inflow forecasts, with different dispatching rules developed for forthcoming inflows of different magnitudes. The reliability analyses of applying this model were conducted by simulating the model by forecasted inflows of 6 extreme flooding years. For the lack of real forecasting data, the so-called forecasted inflows were actually artificially generated by a stochastic model. The risks of applying such a dynamic application model were calculated by generating 9 999 design floods, for segmented 3 flooding stages in a flooding season, and for varying return periods. The obtained design floods were then routed through the dynamic application model, the exceedance frequencies of the results which violating the defined 3 risk events were calculated, and regarded as the risk rates. The results showed that the forecast-based dynamic application model is reliable for at least the routed 6 extreme years, therefore assumed to be also reliable for other normal hydrological years. The calculated risks of applying such a model are also much smaller compared to a research result accomplished previously. Therefore, both reliability and risk analyses indicate the applicability of the developed model under variational FLC conditions for the TGR.

Grouping-Shuffling particle swarm optimization: an improved PSO for continuous optimization

This paper proposes a novel population-based evolution algorithm named grouping-shuffling particle swarm optimization (GSPSO) by hybridizing particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its flight learning evolutionary process By testing on 4 benchmark functions, the numerical results demonstrate that, the optimization performance of the proposed GSPSO is much better than PSO and SFLA The GSPSO can both avoid the PSOs shortage that easy to get rid of the local optimal solution and has faster convergence speed and higher convergence precision than the PSO and SFLA.

A fuzzy multi-objective decision-making model based on vague set and entropy method and its application

A new fuzzy multi-objective decision-making model based on vague set theory and entropy method is presented in this paper. In the proposed model, the alternative schemes are weighted evaluation according to the integrated vague value of its elements relative to the ideal scheme. In order to calculate attributes weights objectively and accurately, a modified entropy weights calculation formula is proposed aiming at the problem of entropy weights be inconsistent with the information of entropy. Finally, the proposed model is applied to solve a practical reservoir flood control decision problem. The results show its rationality and feasibility.

Automatic Calibration of Hydrological Model by Shuffled Complex Evolution Metropolis Algorithm

An accurate forecast of runoff is very significant so that there is ample time for the pertinent authority to issue a forewarning of the impending flood. Due to the highly dimension and nonlinear, the calibration of hydrological model become very complex, so the unique “best” parameter set can not be obtained easily. In this study, an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm(SCEM-UA) is presented, which is well suited to infer the posterior distribution of hydrologic model parameters. This algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Therefore, SCEM-UA is applied in this study to calibrate a lumped Xinanjiang hydrological model having 16 parameters. Three types of data were used for Xinanjiang model: rainfall, evaporation and discharge. One years’ data were used for calibration and one years’ data were used for testing. The criterion used to measure the fitness of the calculated against the observed discharges was the Deterministic Coefficient (DC) and Root Mean Square Error(RMSE). The calibration processes included first of all defining the feasible domains of the model parameters, and initialize the parameters in the feasible domains, then the model parameters were iteratively evaluated and updated, until the terminal condition was satisfied. In order to test the efficiency of the SCEM-UA, Genetic algorithm (GA) is also employed for comparison. The results showed that both calibration and testing results are satisfactory: the DC values of SCEM-UA for the calibration period is 0.79, which is much higher than that of GA, 0.73, and the DC for the testing period is 0.81, which is also better than GA, the same as the RMSE. Visual examinations shows in the high peak flood event, the simulated runoff by SCEM-UA is much better than that by GA.